Shopper Behavior Modeling: How AI Learns What Customers Really Want

Shopper behavior modeling leverages AI to interpret customer actions, such as clicks, searches, and dwell time, to predict real-time purchase intent and preferences, moving beyond traditional collaborative filtering that relies on historical data and struggles with new products and customers. This AI-native approach utilizes embedding models trained on the specific content of a retailer's catalog to understand shopper intent from in-session signals, enabling a personalized shopping experience without needing prior purchase history. The process recognizes five stages of shopper behavior: Discovery, Consideration, Intent, Purchase, and Post-Purchase, with the aim of providing accurate recommendations and enhancing customer interactions throughout these stages. Platforms like Marqo train dedicated AI models on each retailer's catalog, ensuring more precise behavior modeling and overcoming the limitations of generic models. The effectiveness of this approach is measured by metrics such as revenue per session, click-through rates on recommendations, and add-to-cart rates, with the ultimate goal of achieving commerce superintelligence, where AI drives every touchpoint in the customer journey.

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